Receiver-side processing of orthogonal time frequency space modulated signals

ABSTRACT

Wireless communication techniques for transmitting and receiving reference signals is described. The reference signals may include pilot signals that are transmitted using transmission resources that are separate from data transmission resources. Pilot signals are continuously transmitted from a base station to user equipment being served. Pilot signals are generated from delay-Doppler domain signals that are processed to obtain time-frequency signals that occupy a two-dimensional lattice in the time frequency domain that is non-overlapping with a lattice corresponding to data signal transmissions.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent document is a continuation of U.S. application Ser. No.16/084,791, filed Sep. 13, 2018, entitled “RECEIVER-SIDE PROCESSING ORORTHOGONAL TIME FREQUENCY SPACE MODULATED SIGNALS” which is a 371National Phase Application of PCT Application No. PCT/US2017/023892,entitled “RECEIVER-SIDE PROCESSING OF ORTHOGONAL TIME FREQUENCY SPACEMODULATED SIGNALS” filed on Mar. 23, 2017, which claims priority to U.S.Provisional Application Ser. No. 62/312,367 entitled “RECEIVER-SIDEPROCESSING OF ORTHOGONAL TIME FREQUENCY SPACE MODULATED SIGNALS” filedon Mar. 23, 2016. The entire content of the aforementioned patentapplications is incorporated by reference herein.

TECHNICAL FIELD

The present document relates to wireless communication, and moreparticularly, to receiver-side processing of orthogonal time frequencyspace modulated signals.

BACKGROUND

Due to an explosive growth in the number of wireless user devices andthe amount of wireless data that these devices can generate or consume,current wireless communication networks are fast running out ofbandwidth to accommodate such a high growth in data traffic and providehigh quality of service to users.

Various efforts are underway in the telecommunication industry to comeup with next generation of wireless technologies that can keep up withthe demand on performance of wireless devices and networks.

SUMMARY

This document discloses receiver-side techniques for receivingorthogonal time frequency and space (OTFS) modulated signals, andextracting information bits therefrom.

In one example aspect, a wireless communication method, implemented by awireless communications receiver is disclosed. The method includesprocessing a wireless signal comprising information bits modulated usingan orthogonal time frequency and space (OTFS) modulation scheme togenerate time-frequency domain digital samples, performing linearequalization of the time-frequency domain digital samples resulting inan equalized signal, inputting the equalized signal to a feedback filteroperated in a delay-time domain to produce a decision feedback equalizer(DFE) output signal, extracting symbol estimates from the DFE outputsignal, and recovering the information bits from the symbol estimates.

In another example aspect, an apparatus for wireless communication isdisclosed. The apparatus includes a module for processing a wirelesssignal received at one or more antennas of the apparatus. A module mayperform linear equalization in the time-frequency domain. A module mayperform DFE operation in the delay-time domain. A module may performsymbol estimation in the delay-Doppler domain.

These, and other, features are described in this document.

DESCRIPTION OF THE DRAWINGS

Drawings described herein are used to provide a further understandingand constitute a part of this application. Example embodiments andillustrations thereof are used to explain the technology rather thanlimiting its scope.

FIG. 1 shows an example communication network.

FIG. 2 is a block diagram showing an example of an OTFS transmitter.

FIG. 3 is a block diagram showing an example of an OTFS receiver.

FIG. 4 is a block diagram showing an example of a single input multipleoutput (SIMO) DFE receiver.

FIG. 5 is a graph showing an example of multiplexing a pilot region anda data region in the delay-Doppler domain.

FIG. 6 is a block diagram showing an example of a MIMO DFE withordered/unordered inter-stream interference cancellation (SIC).

FIG. 7 is a block diagram showing an example of a MIMO maximumlikelihood (ML) DFE receiver which uses a hard slicer.

FIG. 8 is a block diagram showing an example of a MIMO ML-DFE receiverthat uses soft-QAM modulation.

FIG. 9 shows a flowchart of an example wireless communicationtransmission method.

FIG. 10 shows a block diagram of an example of a wireless transmissionapparatus.

FIG. 11 shows an example of a wireless transceiver apparatus.

FIG. 12 shows an example of a transmission frame.

DETAILED DESCRIPTION

To make the purposes, technical solutions and advantages of thisdisclosure more apparent, various embodiments are described in detailbelow with reference to the drawings. Unless otherwise noted,embodiments and features in embodiments of the present document may becombined with each other.

The present-day wireless technologies are expected to fall short inmeeting the rising demand in wireless communications. Many industryorganizations have started the efforts to standardize next generation ofwireless signal interoperability standards. The 5th Generation (5G)effort by the 3rd Generation Partnership Project (3GPP) is one suchexample and is used throughout the document for the sake of explanation.The disclosed technique could be, however, used in other wirelessnetworks and systems.

Section headings are used in the present document to improve readabilityof the description and do not in any way limit the discussion to therespective sections only.

FIG. 1 shows an example communication network 100 in which the disclosedtechnologies can be implemented. The network 100 may include a basestation transmitter that transmits wireless signals s(t) (downlinksignals) to one or more receivers 102, the received signal being denotedas r(t), which may be located in a variety of locations, includinginside or outside a building and in a moving vehicle. The receivers maytransmit uplink transmissions to the base station, typically locatednear the wireless transmitter. The technology described herein may beimplemented at a receiver 102.

Because OTFS modulated signals are not modulated along a time-frequencygrid but along a delay-Doppler grid, traditional signal receptiontechniques such as those used for receiving orthogonal frequencydivision multiplexing (OFDM) signals, for example, as used in Long TermEvolution (LTE) systems, cannot provide adequate performance to receiveand process OTFS signals to extract or recover information bitsmodulated on the OTFS signals.

The presently disclosed techniques can overcome these problems, andothers.

1. Introduction

Signal transmission over a wireless fading channel undergoes time andfrequency selective fading which must be compensated for reliableend-to-end communication. Contemporary multi-carrier modulationtechniques such as Orthogonal Frequency Division Multiplexing (OFDM) andSingle Carrier Frequency Division Multiplexing (SC-FDM) exploit thedegrees of freedom offered by the channel's frequency selectivity, whichis characterized by the delay spread. However, the time-selective natureof the channel, as characterized by the Doppler spread, is not nativelyhandled by these modulation techniques. Orthogonal Time Frequency andSpace is a generalized two-dimensional multi-carrier modulation thatfully exploits the degrees of freedom offered by the delay and Dopplerdimensions of a wireless channel.

1.1 Notation

The following mathematical notation is adopted in this patent document.

Boldface font are used to describe vectors and matrices. In most caseslower-case and upper-case letters denote vectors and matricesrespectively. In some cases, such as for differentiating time andfrequency vectors, upper-case letters may also be used for vectors inthe frequency domain.

The superscripts (⋅)^(T), (⋅)*, (⋅)^(H) denote, respectively, transpose,conjugate and conjugate transpose operators while ⊗ denotes theKronecker product.

The element in row i and column j of matrix A is denoted as A_(ij) orA(i,j).

The matrix F_(N) denotes a normalized N×N DFT matrix whereF_(N)(i,j)=(1/√{square root over (N)})e^(−j2πij/N).

I_(L), denotes an L×L identity matrix, while O_(L×L)denotes an L×L zeromatrix.

^(M) denotes the M-dimensional vector space over the field of complexnumbers, and x ∈

^(M) represents an M-dimensional column vector.

N_(t), N_(r) are, respectively, the number of transmit and receiveantennas.

N_(l) is the number of spatial layers or streams.

N ,M are the dimensions of the lattice corresponding to the Delay andDoppler axes respectively.

X(k,l) represents a signal at the (k,l) point on the time-frequencygrid, where k is the frequency index and l is the time index.

2. Signal Model

A multi-antenna communication system may include devices transmittingover a wireless fading channel with N_(t) transmit antennas and N_(r)receive antennas. FIG. 2 depicts an example of an OTFS transmitter. Theinformation bits to be communicated from the transmitter may be encodedby a Forward Error Correction (FEC) block, rate matched to the number ofbits the channel allocation can support, scrambled and modulated onto adiscrete constellation denoted as Ω. The information bits may includeuser data that is locally generated or received from other equipment viaa data input connection (not shown in the figure). For clarity, aQuadrature Amplitude Modulation (QAM) constellation example isdiscussed, but it is also possible to use some other digitalconstellation such as Phase Shift Keying.

The QAM symbols are mapped onto one or more spatial layers (or streams)according to the determined channel rank. For example, in downlinkcellular transmission from a base station to a User Equipment (UE), thechannel rank may be computed by the UE and fed back as channel stateinformation (CSI) to the base station. Alternatively, in a Time DivisionDuplex (TDD) system, the base station derives the channel rank byexploiting uplink-downlink channel reciprocity.

For OTFS transmission, the information symbols for layer p can be viewedas functions defined on a two-dimensional Delay-Doppler plane, x(τ, v,p), p=0, . . . , N_(l)−1. The two-dimensional Delay-Doppler channelmodel equation is characterized by a 2D cyclic convolution

$\begin{matrix}{{y( {\tau,v} )} = {{h( {\tau,v} )}_{\;^{\underset{2D}{*}}}{x( {\tau,v} )}}} & (1)\end{matrix}$

where the MIMO channel h(τ,v) is of dimension N_(r)×N_(l) and has finitesupport along the Delay and Doppler axes, and y(τ,v) ∈

^(N) ^(r) is the received noiseless signal. The transmitted vectorx(τ,v) ∈

^(N) ^(l) is assumed to be zero mean and unity variance. Practically,the QAM symbols are mapped onto a lattice by sampling at N points on theτ axis and M points on the v axis, i.e. x(n, m, p), where n=0, . . . ,N−1 and m=0, . . . ,M−1. For simplicity we will omit the layer indexingexcept where necessary.

For each spatial layer, the information symbol matrix is transformed tothe time-frequency domain by a two-dimensional transform. One suchtransform is the inverse Discrete Symplectic Fourier transform (IDSFT).The convention adopted in the present document about Symplectic Fouriertransforms follows the 1-dimensional analogue. (1) (Continuous-time)Fourier transform (FT) <-> Symplectic Fourier transform (SFT). (2)Discrete-time Fourier transform (DTFT) <-> Discrete time-frequencySymplectic Fourier transform (DTFSFT). (3) Discrete Fourier transform(DFT) <-> Discrete Symplectic Fourier transform (DSFT). The IDSFTconverts the effect of the channel on the transmitted signal from atwo-dimensional cyclic convolution in the Delay-Doppler domain to amultiplicative operation in the time-frequency domain. The IDSFToperation is given by the expression:

$\begin{matrix}{{{X( {k,l} )} = {{{IDSFT}\{ {x( {n,m} )} \}} = {\sum\limits_{n = 0}^{N - 1}{\sum\limits_{m = 0}^{M - 1}{{x( {n,m} )}{b_{n,m}^{*}( {k,l} )}}}}}}{{{b_{n,m}( {k,l} )} = {{1/\sqrt{MN}}e^{j2{\pi {({\frac{kn}{N} - \frac{lm}{M}})}}}}},{k = 0},\ldots \;,{N - 1},{l = 0},\ldots \;,{M - 1.}}} & (2)\end{matrix}$

It can be seen from the above that the IDSFT operation produces a 2Dsignal that is periodic in N and M.

Next, a windowing function, C(k,l), may be applied over thetime-frequency grid. This windowing function serves multiple purposes. Afirst purpose is to randomize the time-frequency symbols. A secondpurpose is to apply a pseudo-random signature that distinguishes OTFStransmissions in a multiple access system. :For example, C(k,l) mayrepresent a signature sequence with low cross-correlation property tofacilitate detection in a multi-point-to-point system such as thedownlink of a wireless cellular network.

The spatial layers for each time-frequency grid point may be re-arrangedinto a vector at the input of the spatial precoder. The input to thespatial precoder for the (k,l) grid point is X_(kl)=[X_(k,l)(0), . . . ,X_(k,l)(N_(l)−1)]^(T). The spatial precoder W(k,l) ∈

^(N) ^(t) ^(×N) ^(l) transforms the N_(l) layers to N_(t) streamsmatching the number of transmit antennas. Subsequently, multi-carrierpost-processing is applied, yielding the transmit waveform in the timedomain. FIG. 2 shows an exemplary scheme, wherein a 1D IFFT issequentially applied across the M OTFS time symbols. A cyclic prefix isadded before the baseband signal is sent to a digital-to-analogconverter and up-converted for transmission at the carrier frequency. Ina different method a filter-bank may be applied rather than theIFFT+cyclic prefix method shown in FIG. 2.

FIG. 3 depicts an example of an OTFS receiver. From left to the right ofthe figure, he incoming RF signal is processed through an RF front end,which may include, but is not limited to, down-conversion to basebandfrequency and other required processing such as low pass filtering,automatic frequency correction, IQ imbalance correction, and so on. Theautomatic gain control (AGC) loop and analog-to-digital conversion (ADC)blocks further process the baseband signal for input to the innerreceiver sub-system. The time-and frequency synchronization systemcorrects for differences in timing between the transmitter and receiversub-systems before multi-carrier processing. Herein, the multi-carrierprocessing may consist of cyclic prefix removal and FFT processing toconvert the receive waveform to the time-frequency domain. In adifferent method, a filter-bank may be applied for multi-carrierprocessing.

The received signal at the (k,l) time-frequency grid point is

$\begin{matrix}\begin{matrix}{Y_{kl} = {{{\overset{\sim}{H}}_{kl}{W( {k,l} )}X_{kl}} + Z_{kl}}} \\{= {{H_{kl}X_{kl}} + Z_{kl}}}\end{matrix} & (3)\end{matrix}$

Where {tilde over (H)}_(kl) ∈

^(N) ^(r) ^(×N) ^(t) is the MIMO channel with each entry modeled as acomplex Gaussian random variable [{tilde over (H)}_(kl)]_(ij)˜

(0,1) and H_(kl) ∈

^(N) ^(r) ^(×N) ^(l) is the equivalent channel after spatial precoding.The thermal noise plus other-cell interference at the receiver input,Z_(kl) ∈

^(N) ^(r) is modeled as a complex Gaussian vector Z_(kl)˜

(0, R_(zz)). The received signal vector for N_(r) antennas is given by

Y _(kl)=[Y _(kl)(0), . . . , Y _(kl)(N _(r)−1)]^(T)

3. Linear Equalization

For OFDM systems, the QAM symbols are directly mapped onto thetime-frequency grid. Therefore, per-tone frequency domain MMSEequalization is optimal in the mean square error (MSE) sense. Incontrast, information symbols in an OTFS system are in the Delay-Dopplerdomain. Therefore, per-tone frequency MMSE equalization may besub-optimal. To motivate application of an advanced receiver for OTFSdemodulation we will start with the formulation of a linear MMSEequalizer.

For frequency domain linear equalization, the equalized signal at the(k,l) time-frequency index is given by

{circumflex over (X)}_(kl) ^(MMSE)=G_(kl)Y_(kl)

Applying the Orthogonality theorem the LMMSE filter isG_(kl)=R_(XY)(k,l)R_(YY) ⁻¹(k,l), where

R _(YY)(k,l)=H _(kl) R _(XX)(k,l)H _(kl) ^(H) +R _(ZZ)(k,l)

R _(XY)(k,l)=R _(XX)(k,l)H _(kl) ^(H)

The signal covariance matrix R_(XX)(k,l)=R_(XX) for every k, l, whereasthe receiver noise variance matrix R_(ZZ)(k,l) may be different for eachtime-frequency index. For convenience the time-frequency indices couldbe dropped except where necessary. Using the matrix inversion lemma, theLMMSE (also known as Wiener) filter can be re-written as

G=R _(XX)(I+H ^(H) R _(ZZ) ⁻¹ HR _(XX))⁻¹ H ^(H) R _(ZZ) ⁻¹   (4)

After equalization, a Discrete Symplectic Fourier Transform (DSFT) isperformed to convert the equalized symbols from time-frequency to theDelay-Doppler domain.

The QAM symbols could be considered to reside in the Delay-Dopplerdomain. Thus, time-frequency domain equalization can be shown to besub-optimal. To see this, consider the residual error after LMMSEfiltering, E_(kl)={circumflex over (X)}_(kl) ^(MMSE)−X_(kl), whereE_(kl) ∈

^(N) ^(l) . The corresponding MSE matrix R_(EE)(k,l)∈

^(N) ^(l) ^(N) ^(l) is given by

$\begin{matrix}{{R_{EE}( {k,l} )} = {{E\{ {E_{kl}E_{kl}^{H}} \}} = {R_{XX}( {I + {H_{kl}^{H}{R_{ZZ}^{- 1}( {k,l} )}H_{kl}R_{XX}}} )}^{- 1}}} & (5)\end{matrix}$

Since the equalization is performed independently at each time-frequencyindex, the covariance matrix is independent across the time-frequencygrid. For time index l the error covariance matrix is a block diagonalmatrix where each entry on the diagonal is an N_(l)×N_(l) matrix, i.e.

$\begin{matrix}{{R_{EE}(l)} = \begin{bmatrix}{R_{EE}( {0,l} )} & \ldots & 0_{N_{l} \times N_{l}} \\\vdots & \ddots & \vdots \\0_{N_{l} \times N_{l}} & \ldots & {R_{EE}( {{N - 1},l} )}\end{bmatrix}} & (6)\end{matrix}$

After linear equalization the channel model expression becomes

{circumflex over (X)} _(l) ^(MMSE) =X _(l) +E _(l)   (7)

As the DSFT operation can be decomposed into two one-dimensional DFTtransforms, we start by considering a length N IDFT along the frequencyaxis to the delay domain for OTFS time symbol l. This yields,

$\begin{matrix}\begin{matrix}{{\hat{x}}_{l}^{MMSE} = {F_{N}^{H}{\hat{X}}_{l}^{MMSE}}} \\{{= {x_{l} + e_{l}}},{l = 0},\ldots \mspace{14mu},{M - 1},}\end{matrix} & (8)\end{matrix}$

Where the equalities that x_(l)=F_(N)X_(l) and e_(l)=F_(N)E_(l) areused. The Delay-domain post-equalization error covariance matrix is

$\begin{matrix}\begin{matrix}{{R_{ee}(l)} = {E\{ {e_{l}e_{l}^{H}} \}}} \\{= {F_{N}^{H}E\{ {E_{l}E_{l}^{H}} \} F_{N}}} \\{= {F_{N}^{H}{R_{EE}(l)}F_{N}}}\end{matrix} & (9)\end{matrix}$

The DFT transformation in (9) makes R_(ee)(l) a circulant matrix becauseR_(EE)(l) is a diagonal matrix. This also implies that the errorcovariance matrix is no longer white after transformation to theDelay-domain, i.e. the residual error is correlated. This correlatednoise is caused by ISI which can be seen by re-writing (8) as

$\begin{matrix}\begin{matrix}{x_{l} = {{\hat{x}}_{l}^{MMSE} - e_{l}}} \\{= {{F_{N}^{H}G_{l}Y_{l}} - e_{l}}} \\{= {{A_{l}y_{l}} - e_{l}}}\end{matrix} & (10)\end{matrix}$

where A_(l)=F_(N) ^(H)G_(l)F_(N) is a circulant matrix. A circulantmatrix is characterized by its generator vector, wherein each column ofthe matrix is a cyclic shift of the generator vector. LetA_(l)=[a_(0,l), . . . , a_(N−1,l)]^(T) and, without loss of generality,let a_(0,l) be the generator vector. Then it is straightforward to showthat the signal model above describes a cyclic convolution:

x _(l)(n)=Σ_(m=0) ^(N−1) a _(0,l)(m)y _(l)(n−m)_(mod N)   (10A)

Therefore, ISI is introduced when trying to recover x_(l) from itsestimate. This same reasoning can be extended from the Delay-time domainto the Delay-Doppler domain by computing the second part of the DSFT,namely, a DFT transformation from the time to Doppler domain. This, ineffect is a 2D cyclic convolution that reveals a residual 2Dinter-symbol interference across both Delay and Doppler dimensions. Inthe next section we show how a Decision Feedback Equalizer can be usedto suppress this residual ISI.

4. Decision Feedback Equalization

As the OTFS information symbols reside in the Delay-Doppler domain,where the channel effect on the transmitted signal is a 2D cyclicconvolution, a 2D equalizer is desirable at the receiver. One method ofimplementing a 2D equalizer is as follows. In a first step, a linearequalizer is applied in the time-frequency domain—as described in theprevious section. As a second step, a feedback filter is applied in theDelay-Doppler domain to mitigate the residual interference across bothdelay and Doppler axes. However, since the OTFS block transmission iscyclic, the residual ISI on a particular QAM symbol is caused by otherQAM symbols across the Delay-Doppler plane in the current N×Mtransmission block. It may be difficult from an implementationperspective to mitigate ISI in a full 2D scheme. The complexity of a 2Dfeedback filter for a DFE can be reduced by employing a hybrid DFE.Specifically, (1) The feedforward filter is implemented in thetime-frequency domain, (2) the feedback filter is implemented in theDelay-time domain, and (3) the estimated symbols are obtained in theDelay-Doppler domain.

The rationale for this approach is that after the feedforward filtering,the residual ISI in the Delay domain dominates the interference in theDoppler domain. A set of M parallel feedback filters are implementedcorresponding to the M time indices in the OTFS block. This documentdiscloses a DFE receiver for a single input multiple output (SIMO)antenna system (which includes the case of a single receive antenna)system and then extends to the more general multiple input multipleoutput (MIMO) case, where multiple data streams are transmitted.

4.1 SIMO-DFE

The input to the feedback filter is given by (8) where for the SIMO casex_(l)∈

^(N). A set of M parallel noise-predictive DFE feedback filters areemployed in the Delay-time domain. For time index l, the estimation ofx_(l)(n), n=0, . . . , N−1, is based on exploiting the correlation inthe residual error. Given the (LMMSE) feedforward output signal

x _(l) ^(MMSE)(n)=x _(l)(n)+e _(l)(n)   (10C)

Some embodiments may be implemented to seek a predicted error signalê_(l)(n) such that the variance of the error term, x_(l)^(MMSE)(n)−ê_(l)(n) is reduced before estimation. The closer ê_(l)(n) isto e_(l)(n) , the more accurate would be the final detection ofx_(l)(n). For simplicity, it may be assumed that the residual error fromμ past detected symbols is known. Then the predicted error at the nthsymbol is given by:

$\begin{matrix}{{{\hat{e}}_{l}(n)} = {\sum\limits_{m = 1}^{\mu}{b_{m}{e_{l}( {n - m} )}}}} & (11)\end{matrix}$

Where {b_(m)} are the error prediction filter coefficients. Forsimplicity, the analysis below drops the time index l. The expressionabove for symbol n can be put in a block processing form by re-writingthe error vector at symbol n as ê_(n)=[ê_(n−μ), ê_(n−μ+1), . . . ,ê_(n)]^(T). Thus, it can be seen that:

ê_(n)=Be_(n),   (12)

where B ∈

^((μ+1)×(μ+1)) is a strictly lower triangular matrix (i.e. zero entrieson the diagonal) with the last row given by b_(μ)=[b_(μ,1), . . . ,b_(μ,μ), 0] and e_(n)=[e_(n−μ), . . . , e_(n−1), e_(n)]^(T).

This predictive error formulation depends on the filter length μ+1. Assuch, in some implementations, the pre-feedback error covariance matrixR_(ee) may be truncated based on this feedback filter length. Takinginto account the cyclic (or periodic) nature of (10) the truncated errorcovariance matrix for symbol n is given by the sub-matrix:

$\begin{matrix}\begin{matrix}{{\overset{\sim}{R}}_{ee} = {{trun}c\{ R_{ee} \}}} \\{= {R_{ee}\lbrack {( {{n - \mu},\ldots \mspace{14mu},n} )_{{mod}\mspace{14mu} N},( {{n - \mu},\ldots \mspace{14mu},n} )_{{mod}\mspace{14mu} N}} \rbrack}}\end{matrix} & (13)\end{matrix}$

The final DFE output is then given by

{circumflex over (x)} ^(DFE)(n)={circumflex over (x)} ^(MMSE)(n)−b _(μ)e _(n) , n=0, . . . , N−1   (14)

Typically, past residual errors are unknown because the receiver onlyhas access to the output of the feedforward equalizer output {circumflexover (x)}^(MMSE)(n), n=0, . . . , N −1. Assuming that past harddecisions {circumflex over (x)}^(h)(n−μ), . . . , {circumflex over(x)}^(h)(n−1)} are correct, some implementations can form an estimate ofê(n−i) as:

ê(n−i)={circumflex over (x)} ^(MMSE)(n−i)−{circumflex over (x)}^(h)(n−i), i=1, . . . , μ  (15)

This document also discloses how reliable past decisions can beobtained. The residual error at the output of the feedback filter isthen given by:

$\begin{matrix}\begin{matrix}{ɛ_{n} = {e_{n} - {\hat{e}}_{n}}} \\{{= {( {I_{\mu} - B} )e_{n}}},{n = 0},\ldots \mspace{14mu},{N - 1}}\end{matrix} & (16)\end{matrix}$

The resulting error covariance matrix is:

$\begin{matrix}\begin{matrix}{R_{ɛɛ} = {E\{ {ɛ_{n}ɛ_{n}^{H}} \}}} \\{= {( {I_{\mu} - B} ){{\overset{\sim}{R}}_{ee}( {I_{\mu} - B} )}^{H}}}\end{matrix} & (17)\end{matrix}$

The Cholesky decomposition of {tilde over (R)}_(ee) is:

{tilde over (R)}_(ee)=LDU

where L is a lower triangular matrix with unity diagonal entries, D is adiagonal matrix with positive entries and U=L^(H) is an upper triangularmatrix. Substituting this decomposition into (17), it is straightforwardto show that the post DFE error covariance is minimized if

L ⁻¹ =I _(μ) −B   (18)

Where B is a strictly lower triangular matrix

$B = \begin{bmatrix}0 & 0 & 0 & 0 & 0 & 0 \\b_{10} & 0 & 0 & 0 & 0 & 0 \\\vdots & \ddots & \ddots & 0 & 0 & 0 \\\vdots & \vdots & \ddots & \ddots & 0 & 0 \\b_{{\mu - 1},0} & \ldots & \ldots & \ldots & 0 & 0 \\b_{\mu,0} & b_{\mu,1} & \ldots & \ldots & b_{\mu,{\mu - 1}} & 0\end{bmatrix}$

FIG. 4 is a block diagram of an example embodiment of a DFE receiver fora SIMO system. For each time slice l ∈ {0, . . . , M−1} the feedbacksection works on each sample n ∈ {0, . . . , N−1}. It can be seen inFIG. 4 that the feedback filter 402 works in parallel across each timeslice l=0, . . . , M−1. The Delay-time DFE output is transformed to theDelay-Doppler domain and sent to the soft QAM demodulator (soft slicer),which produces log likelihood ratios (LLRs) as soft input to the FECdecoder.

To start the feedback at n=0, the past symbols {n−μ, . . . , n−1} areactually modulo N, i.e. they are the last portion of the length N datablock, for which hard decisions are not yet available. In someembodiments, a hard decision is made on the output of the feedforwardfilter. Alternatively or additionally, in some embodiments, a knownsequence is appended at the end of each transmitted block, which alsohelps mitigate error propagation. For example, data and pilot regionsmay be multiplexed in the Delay-Doppler domain as shown in the examplegraph in FIG. 5. Here, the pilot region consists of a single impulse at(0, 0) and a zero-power region at the edges of the data region tofacilitate estimation of the Delay-Doppler channel response at thereceiver. This pilot region constitutes a known sequence that can beused to start the feedback filter.

In some embodiments, the transmitted signals may include a framestructure in which the lowest constellations are sent at the top(beginning) of a frame, in the delay domain. FIG. 12 show an example ofmultiplexing information bits for different users in a transmission on astream-basis such that a pilot signal transmission portion may be at thebeginning of the transmission frame, followed by the lowest modulation(4QAM, in this case), followed by increasing modulation for differentusers based on the channel condition to the corresponding userequipment. The data being sent to different users may thus be arrangedalong the delay dimension.

As shown the example of FIG. 12, the transmitted wireless signal mayinclude one or more streams (spatial layers). Each stream may include afirst portion that includes a decision feedback equalization signal,followed by a second portion in which data being transmitted (e.g.,modulation information bits) to multiple user equipment is arranged inincreasing level of modulation constellation density along the delaydimension.

In some implementations, the DFE algorithm may be described as follows:(1) Compute the time-frequency LMMSE (feedforward) equalizer output. (2)For the 1^(th) OTFS symbol, transform the LMMSE equalizer output toDelay-time domain to obtain (8). (3) Compute the delay-domain errorcovariance matrix R_(ee)(l)=F_(N) ^(H)R_(EE)(l)F_(N). In someimplementation, rather than performing the full matrix multiplications,a faster method may be used. (4) Computing the truncated errorcovariance matrix in (13). (5) Obtaining the filter b_(μ) as the lastrow of B=I_(μ)−L⁻¹. (6) DFE output for sample n is

${{\hat{x}}^{DFE}(n)} = {{{\hat{x}}^{MMSE}(n)} - {\sum\limits_{i = 1}^{\mu}{b_{\mu,i}( {{{\hat{x}}^{MMSE}( {n - i} )}_{{mod}\mspace{14mu} N} - {{\hat{x}}^{h}( {n - i} )}_{{mod}\mspace{14mu} N}} )}}}$

(6) Collecting all time slices and transform to the Delay-Dopplerdomain.

4.2 MIMO-DFE

In some embodiments, a MIMO DFE technique could be largely based on theSIMO case but with some differences. First, the expressions in the SIMOcase still hold but with the difference that each element of a vector ormatrix is now of dimension N_(l). For instance each element of the(μ+1)×(μ+1) covariance matrix of (13) is an N_(l)×N_(l) matrix. Second,while the cancellation of past symbols eliminates, or at leastmitigates, the ISI, there is still correlation between the MIMO streams.It can be shown that, by design, the noise-predictive MIMO DFE structurealso performs successive inter-stream interference cancellation (SIC).In the present case, the cancellation between streams may be ordered orun-ordered. This document describes both these cases separately andshows an extension of the DFE receiver to incorporate a near maximumlikelihood mechanism.

4.3 MIMO DFE with SIC

FIG. 6 depicts an example embodiment of a MIMO DFE receiver. The LMMSEfeedforward output {circumflex over (x)}^(MMSE)(n) is a vector ofdimension N_(l)>1. Similarly to the SIMO (N_(l)=1) case, the MIMO DFEworks in parallel across the time axis. For convenience, the time indexl=0, . . . , M−1 are omitted. To detect the data vector at the nth delayindex x_(n) for any time index, arrange the observation vector from thefeedforward filter output of (8) first according to spatial layers andthen according to the delay domain as:

$\begin{matrix}{{\hat{x}}^{MMSE} = \begin{bmatrix}{\hat{x}}_{{n - \mu},0}^{MMSE} \\\vdots \\{\hat{x}}_{{n - \mu},{N_{l} - 1}}^{MMSE} \\\vdots \\\vdots \\{\hat{x}}_{n,0}^{MMSE} \\\vdots \\{\hat{x}}_{n,{N_{l} - 1}}^{MMSE}\end{bmatrix}} & (19)\end{matrix}$

The frequency-domain error covariance matrix of (6) is a block diagonalmatrix, where each diagonal element R_(EE)(n, n)∈

^(N) ^(l) ^(×N) ^(l) . Define the block N×N DFT matrix as

{tilde over (F)}_(N)=F_(N)⊗I_(N) _(l)   (20)

Then, it is straightforward to show that the corresponding delay-domainerror covariance is given by:

R_(ee)={tilde over (F)}_(N) ^(H)R_(EE){tilde over (F)}_(N)   (21)

Similar to the SIMO case, the columns of R_(ee) can be obtained by anN_(l)×N_(l) block circular shift of the generator vector R_(ee)[0] ∈

^(N·N) ^(l) ^(×N) ^(l) .

Again, implementations can obtain the truncated covariance matrix of(13), and after Cholesky decomposition, the lower triangular matrix isof the form:

$L = \begin{bmatrix}L_{0,0} & 0 & 0 & 0 & 0 \\L_{10} & L_{11} & 0 & 0 & 0 \\\vdots & \vdots & \ddots & 0 & 0 \\\vdots & \vdots & \ddots & \ldots & 0 \\L_{{\mu - 1},0} & \ldots & \ldots & \ldots & 0 \\L_{\mu,0} & L_{\mu,1} & \ldots & \ldots & L_{\mu,\mu}\end{bmatrix}$

Each diagonal entry of L is an N_(l)×N_(l) lower triangular matrix. Thefeedback filter is taken as the last block row of the B matrix obtainedas in (18) but now for the MIMO case. Hence, the matrix feedback filterb_(μ)∈

^(N) ^(l) ^(×N) ^(l) ^((μ+1)) is given by:

b _(μ) =[b _(μ,0) , b _(μ,1) , . . . , b _(μ,μ)]  (22)

The last block element b_(μμ) is strictly lower triangular. To see theeffect of the inter-stream cancellation, consider the 2×2 case. The lastblock element of the feedback filter is given by

$\begin{matrix}{b_{\mu,\mu} = \begin{bmatrix}0 & 0 \\\alpha & 0\end{bmatrix}} & (23)\end{matrix}$

From (19) the current symbol vector to be detected isx_(n)=[x_(n,0)x_(n,1)]^(T). From the product b_(μ,μ)·e_(n) which isperformed in (14) it can be seen that for the feedback filter does notact on the error in the first layer, while for the second layer, thereis a filter coefficient acting on the first layer.

The interpretation may be as follows: for the first layer a predictionerror is computed only from hard decisions of past symbol vectors. Forthe second layer, the detection of the first layer is used to predicterror for detecting the second layer. More generally, detection of aspatial layer for a current symbol vector utilizes hard decisions frompast detected symbol vectors as well as hard decisions for precedinglayers in the current symbol. This is equivalently an SIC mechanismwithout any ordering applied to the stream cancellation. A differentmethod is to apply ordering across the spatial layers in the MIMO systemin scenarios where the SINR statistics are not identical across spatiallayers.

4.4 MIMO DFE with Maximum Likelihood Detection

A different method to the DFE is to only cancel the ISI from past symbolvectors. That is, to detect x_(n), implementations can use anobservation vector of v_(n,past)=[v_(n−μ) ^(T), v_(n−μ−1) ^(T), . . . ,v_(n−1) ^(T)]^(T) to form the prediction error vector for (14). If thecancellation of ISI is perfect, the post DFE signal expression for then^(th) symbol vector

{circumflex over (x)} _(n) ^(DFE) =x _(n) +e _(n tm ()24)

is now similar to what is expected in say OFDM, where the interferenceis only between the spatial layers (or streams). Therefore,implementations can apply a maximum likelihood receiver to detect theQAM symbols on each layer.

FIG. 7 depicts an example embodiment of a DFE-ML receiver, where a hardslicer is applied for the input to the DFE feedback path. Given a QAMconstellation Ω, the ML decision is

$\begin{matrix}{x_{n}^{ML} = {{\underset{u \in \Omega^{N_{l}}}{argmax}( {u - {\hat{x}}_{n}^{DFE}} )}^{H}{R_{ee}^{- 1}( {u - {\hat{x}}_{n}^{DFE}} )}}} & (25)\end{matrix}$

The error covariance matrix, R_(ee) corresponds to the additive error in(24) that is obtained after cancelling interference from past symbols.Furthermore, R_(ee) is not white. To improve detection performance, someembodiments may first whiten the ML receiver input as follows. First,decompose the error covariance matrix as R_(ee)=R_(ee) ^(1/2)R_(ee)^(T/2). Then, let the input to the ML receiver be

$\begin{matrix}\begin{matrix}{{R_{ee}^{{- 1}/2}{\hat{x}}_{n}^{DFE}} = {R_{ee}^{{- 1}/2}( {x_{n} + e_{n}} )}} \\{= {{R_{ee}^{{- 1}/2}x_{n}} + {\overset{\sim}{e}}_{n}}}\end{matrix} & (26)\end{matrix}$

This expression now follows the basic MIMO equation for ML, i.e. y=Hx+n,where, in our case, the channel H=R_(ee) ^(−1/2)x_(n) and the noisecovariance matrix E{{tilde over (e)}_(n){tilde over (e)}_(n) ^(H)}=I_(N)_(l) .

In addition, the ML provides a log likelihood ratio (LLR) for eachtransmitted bit. Rather than resort to hard QAM decisions for the DFE, adifferent method is to generate soft QAM symbols based on the LLR valuesfrom the ML receiver.

FIG. 8 illustrates an example embodiment of a DFE-ML receiver with softinput to the feedback path. While all other functional blocks aresimilar to FIG. 7, in place of a hard slicer, a soft QAM modulator maybe used to provide input to the IDFT operation in the feedback path.

FIG. 9 is a flowchart for an example method 200 of wirelesscommunication. The method 200 may be implemented by a wireless receiver,e.g., receiver 102 depicted in FIG. 1.

The method 200 includes, at 202, processing a wireless signal comprisinginformation bits modulated using an OTFS modulation scheme to generatetime-frequency domain digital samples. In some embodiments, thetime-frequency domain samples may be generated by applying atwo-dimensional transform to the wireless signal. The two-dimensionaltransform may be, for example, a discrete Symplectic Fourier transform.In some embodiments, the two-dimensional transform may be applied bywindowing over a grid in the time-frequency domain.

In some embodiments, the processing 202 may be performed using an RFfront end which may downcovert the received signal from RF to basebandsignal. Automatic Gain Control may be used to generate an AGC-correctedbaseband signal. This signal may be digitized by an analog to digitalconverter to generate digital samples.

The method 200 includes, at 204, performing linear equalization of thetime-frequency domain digital samples resulting in an equalized signal.Various embodiments of linear equalization are described in thisdocument. In some embodiments, the linear equalization may be performedusing a mean square error criterion and minimizing the error. Someexamples are given with reference to Eq. (4) to Eq. (9). In someembodiments, a Wiener filtering formulation may be used for theoptimization.

The method 200 further includes, at 206, inputting the equalized signalto a feedback filter operated in a delay-time domain to produce adecision feedback equalizer (DFE) output signal. Various possibilitiesof DFE include single-input, multiple output (SIMO) DFE (Section 4.1),multiple-input multiple-output (MIMO) DFE (Section 4.2), MIMO-DFE withsuccessive interference cancellation (Section 4.3), and MIMO DFE withmaximum likelihood estimation (Section 4.4), as described herein.

The method 200 further includes, at 208, extracting symbol estimatesfrom the DFE output signal. As described with reference to FIGS. 2-4 and6-8, in some embodiments, the extraction operation may be performed inthe delay-Doppler domain.

The method 200 further includes, at 210, recovering the information bitsfrom the symbol estimates. The symbols may be, for example, quadratureamplitude modulation symbols such as 4, 8, 16 or higher QAM modulationsymbols.

In some embodiments, a wireless signal transmission method may includegenerating data frames, e.g., as depicted in FIG. 12, and transmittingthe generated data frames to multiple UEs over a wireless communicationchannel. For example, the transmission method may be implemented at abase station in a wireless network.

FIG. 10 is a block diagram showing an example communication apparatus300 that may implement the method 200. The apparatus 300 includes amodule 302 for processing a wireless signal received at one or moreantennas of the apparatus 300. A module 304 may perform linearequalization in the time-frequency domain. The module 306 may performDFE operation in the delay-time domain. The module 308 may performsymbol estimation in the delay-Doppler domain. The module 310 mayrecover information bits from modulated symbols.

FIG. 11 shows an example of a wireless transceiver apparatus 600. Theapparatus 600 may be used to implement method 200. The apparatus 600includes a processor 602, a memory 604 that stores processor-executableinstructions and data during computations performed by the processor.The apparatus 600 includes reception and/or transmission circuitry 606,e.g., including radio frequency operations for receiving or transmittingsignals.

It will be appreciated that techniques for wireless data reception aredisclosed using two-dimensional reference signals based on delay-Dopplerdomain representation of signals.

The disclosed and other embodiments, modules and the functionaloperations described in this document can be implemented in digitalelectronic circuitry, or in computer software, firmware, or hardware,including the structures disclosed in this document and their structuralequivalents, or in combinations of one or more of them. The disclosedand other embodiments can be implemented as one or more computer programproducts, i.e., one or more modules of computer program instructionsencoded on a computer readable medium for execution by, or to controlthe operation of, data processing apparatus. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, a composition of matter effecting amachine-readable propagated signal, or a combination of one or morethem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this document can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of non-volatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

While this patent document contains many specifics, these should not beconstrued as limitations on the scope of an invention that is claimed orof what may be claimed, but rather as descriptions of features specificto particular embodiments. Certain features that are described in thisdocument in the context of separate embodiments can also be implementedin combination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asub-combination or a variation of a sub-combination. Similarly, whileoperations are depicted in the drawings in a particular order, thisshould not be understood as requiring that such operations be performedin the particular order shown or in sequential order, or that allillustrated operations be performed, to achieve desirable results.

Only a few examples and implementations are disclosed. Variations,modifications, and enhancements to the described examples andimplementations and other implementations can be made based on what isdisclosed.

What is claimed is:
 1. A method for wireless communication, comprising:receiving, by a wireless communication receiver, a wireless signalcomprising information bits that are modulated using an orthogonal timefrequency and space (OTFS) modulation scheme; generating a basebandsignal from the wireless signal; generating digital samples thatcorrespond to an output of applying a discrete Symplectic Fouriertransform or an inverse discrete Symplectic Fourier transform to thebaseband signal; performing a linear equalization of the digital samplesresulting in an equalized signal; producing a decision feedbackequalizer (DFE) output signal by inputting the equalized signal to afeedback filter; extracting symbol estimates from the DFE output signal;and recovering the information bits from the symbol estimates.
 2. Themethod of claim 1, wherein the generating the baseband signal comprises:down-converting the wireless signal from a radio frequency signal togenerate the baseband signal.
 3. The method of claim 2, furthercomprising: applying automatic gain control (AGC) to the baseband signalto generate a gain-corrected baseband signal.
 4. The method of claim 3,further comprising: converting the gain-corrected baseband signal froman analog domain to the digital samples in a time-frequency domain. 5.The method of claim 1, wherein the performing the linear equalizationcomprises performing the linear equalization to minimize a mean squareerror of an error criterion.
 6. The method of claim 1, wherein theequalized signal comprises inter-symbol-interference (ISI), and whereinthe DFE output signal suppresses the ISI.
 7. The method of claim 1,wherein the producing the DFE output signal comprises performing amulti-input multi-output (MIMO) decision feedback equalization (DFE) toproduce the DFE output signal.
 8. The method of claim 1, the producingthe DFE output signal comprises performing a multi-input multi-output(MIMO) decision feedback equalization (DFE) using successiveinterference cancellation (SIC) to produce the DFE output signal.
 9. Anapparatus for wireless communication, comprising: a receiver configuredto receive a wireless signal comprising information bits that aremodulated using an orthogonal time frequency and space (OTFS) modulationscheme; and a processor, coupled to the receiver, configured to:generate a baseband signal from the wireless signal; generate digitalsamples that correspond to an output of applying a discrete SymplecticFourier transform or an inverse discrete Symplectic Fourier transform tothe baseband signal; perform a linear equalization of the digitalsamples resulting in an equalized signal; produce a decision feedbackequalizer (DFE) output signal by inputting the equalized signal to afeedback filter; extract symbol estimates from the DFE output signal;and recover the information bits from the symbol estimates.
 10. Theapparatus of claim 9, wherein the processor is further configured, aspart of generating the baseband signal, to: down-convert the wirelesssignal from a radio frequency signal to generate the baseband signal.11. The apparatus of claim 10, wherein the processor is furtherconfigured to: apply automatic gain control (AGC) to the baseband signalto generate a gain-corrected baseband signal.
 12. The apparatus of claim9, wherein performing the linear equalization comprises performing thelinear equalization to minimize a mean square error of an errorcriterion.
 13. The apparatus of claim 9, wherein the performing thelinear equalization includes performing Wiener filtering of the digitalsamples.
 14. The apparatus of claim 9, wherein the equalized signalcomprises inter-symbol-interference (ISI), and wherein the DFE outputsignal suppresses the ISI.
 15. A method for wireless communication,comprising: receiving, by a wireless communication receiver, a wirelesssignal comprising information bits that are modulated using anorthogonal time frequency and space (OTFS) modulation scheme; generatinga baseband signal from the wireless signal; generating digital samplesthat correspond to an output of applying a two-dimensional windowingfunction to the baseband signal; performing a linear equalization of thedigital samples resulting in an equalized signal; producing a decisionfeedback equalizer (DFE) output signal by inputting the equalized signalto a feedback filter; extracting symbol estimates from the DFE outputsignal; and recovering the information bits from the symbol estimates.16. The method of claim 15, wherein the two-dimensional windowingfunction is applied over a grid in a time-frequency domain.
 17. Themethod of claim 15, wherein the performing the linear equalizationcomprises performing the linear equalization to minimize a mean squareerror of an error criterion.
 18. The method of claim 15, wherein theequalized signal comprises inter-symbol-interference (IR), and whereinthe DFE output signal suppresses the ISI.
 19. The method of claim 15,wherein the producing the DFE output signal comprises performing amulti-input multi-output (MIMO) decision feedback equalization (DFE) toproduce the DFE output signal.
 20. The method of claim 15, the producingthe DFE output signal comprises performing a multi-input multi-output(MIMO) decision feedback equalization (DFE) using successiveinterference cancellation (SIC) to produce the DFE output signal.